AI Simply Explained’s Post

🌟 Exciting News from AI Simply Explained! 🌟 We're thrilled to announce the launch of our Watermarking LLMs 101 Notebook, where we delve into research papers on watermarking but with a simplified twist. Check it out here: https://lnkd.in/gRdMF6bE Additionally, we've open-sourced our GitHub repository here: https://lnkd.in/gaKRaKs2! It will serve as a continuously updated resource, featuring an ever-expanding list of research papers focused on Watermarking LLMs.

Unveiling the Mystery of AI Hallucinations! 🚀 Ever wondered why LLMs sometimes give answers that sound right but are completely off? 🤔 What's Happening? When the LLM encounters something new or "unfamiliar", it tends to make a "safe guess" based on past examples. It's like if you've only ever seen red apples and someone asks about a blue apple, you might guess it's somewhat like the red ones but not exactly. 🎯 Taking Control: By tweaking the LLM's learning examples, we can teach it to respond with "I don't know" to unfamiliar questions instead of hallucinating. 🔍 In Practice: The research team used reinforcement learning, a type of training based on a reward system, to reduce these hallucinations in long responses, like stories or articles. They made the LLM more reliable by adjusting its reward system to favor accuracy and admission of ignorance over making things up. Read More: https://lnkd.in/g474wg9V #ai #machinelearning #llms #artificialintelligence #research

Thank you all!

Watermark Security in LLMs not so secure?!🔒 💡Watermark Stealing: Imagine a digital "watermark" that helps identify who created a text. Now, imagine someone sneaking in, figuring out how this watermark works, and using it to create or alter texts falsely under the creator's name. This is exactly what the research team discovered could be done with LLMs - and for less than $50! 🎭 The Spoofing Threat a security loophole allows bad actors to create high-quality, fake texts that seem like they're from the original watermarking model. This can harm the model's reputation if false information or harmful content is spread. 🧽 Scrubbing: These attackers can remove watermarks from genuine content, making it hard to trace back to the model. This could hide plagiarism or other misuses. 🛑What's the Big Deal?: If watermarks can be easily fooled or removed, it's like having a lock that can be picked by anyone. This challenges the security and reliability of LLMs, which we rely on for authentic content generation. As we venture further into the AI era, understanding and improving security measures like watermarking is crucial. Let's stay informed and push for safer, more reliable AI technologies! Read More: https://lnkd.in/gsrzfDpA #AI #LLM #llmsecurity #researchpaper

Did you know you could use 😶🌫️hallucinations😶🌫️ to increase the accuracy of llms? Sounds kinda counterintuitive right? Well it isn't. A recent research paper introduces an innovative approach called null-shot prompting, which leverages hallucinations in large language models (LLMs) to enhance their performance. Unlike traditional methods that aim to reduce hallucinations, null-shot prompting instructs LLMs to use information from a non-existent "Examples" section in the provided context to perform tasks. This approach has shown improvements in various tasks, such as reading comprehension, arithmetic reasoning, and closed-book question answering, across multiple LLMs. This research highlights the potential of exploiting hallucinations in LLMs to improve their task performance. You can read more here: https://lnkd.in/gpxafc-b

Teaching an LLM an entirely new language just by prompting?! 🤔 Well, this research paper did just that. Existing large language models struggle to support languages with very minimal training data, like Zhuang a language supported by no LLMs currently. They introduced DIPMT++, a framework that adapts to unseen languages by incontext learning: the ability to learn and adapt to new information or tasks directly from the examples provided within the current context, without the need for additional training or updates to the model's parameters This enhances the performace of GPT-4 from 0 to16 BLEU (a metric used to evaluate machine-translated text, and stands for Bilingual Evaluation Understudy) for Chinese-to-Zhuang translation and 0 to 32 BLEU for Zhuang-to-Chinese translation. Read More here: https://lnkd.in/gkNv7Eqi

Hack LLMs using ASCII Art! A recent research paper demonstrates how rewriting your query using ASCII art allows you to pass LLM guardrails! Read more here: https://lnkd.in/gwWWepjm